The invention relates generally to modeling customer behavior. In particular, the invention relates to developing a predictive model for a customer's behavior by coarse-classing a variable in the predictive model.
Good customer relationship management (CRM) has become a valuable tool in today's highly competitive markets. CRM enables a business to know who its customers are, what its customers buy, how much its customers earn, how much its customers spend and other similar types of information that allow a company to understand the market for its goods and/or services. This information helps a company to predict certain events in the future, such as predicting how much profit a bank may make from a business activity. This information also helps a company to find out the propensity of its customer for a particular behavior, such as how likely the customer is to respond to an offer, how likely the customer is to default on a loan, or pay a loan earlier than scheduled, etc. One method of predicting customer behavior that businesses have utilized is a predictive model.
A predictive model attempts to predict a future result based on past experience. As noted above, the result of the predictive model could be a predicted profit from a business activity. For example, a predictive model may be developed to estimate a bank's annual profit from its credit card business. The result of a predictive model may be any real number. A subset of predictive modeling is propensity modeling. A propensity model provides an estimate of the propensity of a customer to respond to a particular event in a particular manner. Thus, a propensity model is based on a binary situation, either an event occurs or it does not. Typically, a predictive model is based on previous experiences with existing customers. For this purpose, companies maintain databases of their customers replete with data from previous transactions, conduct surveys, or customer response sheets. For example, the predictive model may be based on the customer's age, the customer's income, how a customer responded to a similar event in the past, and/or many other customer attributes. Each of these attributes may be a variable used in the predictive model. In addition, a coefficient may be used to weight each of the attributes to provide the best model of the customer's behavior. For example, the customer's age may be more indicative than the customer's income as to whether or not a customer will respond in a particular manner to an event. Consequently, the coefficient for the customer's age would be greater than the coefficient for the customer's income.
One technique that may be used in developing a predictive model is the coarse-classing of one or more of the customer attributes used in a propensity model. This is desirable when the variable has a non-linear relationship with the response. For example, the propensity of a customer to respond in a particular manner may not be a linear function of the customer's age, i.e., a customer forty years of age may not be twice as likely to respond in a given manner to an event as a twenty year old. However, the age of the customer may still be a good predictor of the customer's behavior. Therefore, by coarse-classing the customer's into two or more age ranges, a different coefficient may be assigned to each of these classes of customers.
There are two existing methods for coarse-classing variables in a predictive model. In the first method, the classes are created using domain knowledge. In the second method, the dataset is divided into equal sized quantiles (e.g. Deciles) based on the range of the attribute under consideration. For each quantile, the information value is calculated based on the number of events and non-events in that quantile. The quantiles are then grouped based on a visual inspection. While the first approach relies on a good understanding of the domain, the second approach is constrained by the fact that the starting solutions are equal sized quantiles, which might not lead to an optimal partitioning of the range.
Therefore, there is a need for a technique that improves the development of coarse-classes in a predictive model. In particular, a technique is desired that would incorporate existing domain knowledge, but not be restricted by it.